{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>vol</th>\n",
       "      <th>value</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>HSI</td>\n",
       "      <td>21860.04</td>\n",
       "      <td>22024.83</td>\n",
       "      <td>21689.22</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>21872.50</td>\n",
       "      <td>48509170000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22092.15</td>\n",
       "      <td>22297.04</td>\n",
       "      <td>21987.27</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>21823.28</td>\n",
       "      <td>82973500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22357.46</td>\n",
       "      <td>22514.79</td>\n",
       "      <td>22277.13</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>22279.58</td>\n",
       "      <td>91328340000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22548.03</td>\n",
       "      <td>22169.61</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>22416.67</td>\n",
       "      <td>79167640000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.006567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>HSI</td>\n",
       "      <td>22282.75</td>\n",
       "      <td>22443.22</td>\n",
       "      <td>22206.16</td>\n",
       "      <td>22296.75</td>\n",
       "      <td>22269.45</td>\n",
       "      <td>71931720000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      open      high       low     close  preclose  \\\n",
       "time                                                                  \n",
       "2010-01-04    HSI  21860.04  22024.83  21689.22  21823.28  21872.50   \n",
       "2010-01-05    HSI  22092.15  22297.04  21987.27  22279.58  21823.28   \n",
       "2010-01-06    HSI  22357.46  22514.79  22277.13  22416.67  22279.58   \n",
       "2010-01-07    HSI  22548.03  22548.03  22169.61  22269.45  22416.67   \n",
       "2010-01-08    HSI  22282.75  22443.22  22206.16  22296.75  22269.45   \n",
       "\n",
       "                    vol  value       ret  \n",
       "time                                      \n",
       "2010-01-04  48509170000    0.0 -0.002250  \n",
       "2010-01-05  82973500000    0.0  0.020909  \n",
       "2010-01-06  91328340000    0.0  0.006153  \n",
       "2010-01-07  79167640000    0.0 -0.006567  \n",
       "2010-01-08  71931720000    0.0  0.001226  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfhsi = pd.read_csv('datasets/hsi.csv', index_col=['time'], parse_dates=[\"time\"], \n",
    "                    date_parser=lambda x: datetime.datetime.strptime(x[:-10], \"%Y-%m-%d\"))\n",
    "dfhsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>vol</th>\n",
       "      <th>value</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-19</th>\n",
       "      <td>HSI</td>\n",
       "      <td>28960.13</td>\n",
       "      <td>29319.76</td>\n",
       "      <td>28806.76</td>\n",
       "      <td>29106.15</td>\n",
       "      <td>28969.71</td>\n",
       "      <td>12486701991</td>\n",
       "      <td>1.501631e+11</td>\n",
       "      <td>0.004710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20</th>\n",
       "      <td>HSI</td>\n",
       "      <td>28962.80</td>\n",
       "      <td>29220.19</td>\n",
       "      <td>28885.55</td>\n",
       "      <td>29135.73</td>\n",
       "      <td>29106.15</td>\n",
       "      <td>12337814847</td>\n",
       "      <td>1.957113e+11</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-21</th>\n",
       "      <td>HSI</td>\n",
       "      <td>28702.27</td>\n",
       "      <td>28778.36</td>\n",
       "      <td>28506.76</td>\n",
       "      <td>28621.92</td>\n",
       "      <td>29135.73</td>\n",
       "      <td>10974966582</td>\n",
       "      <td>1.543542e+11</td>\n",
       "      <td>-0.017635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>HSI</td>\n",
       "      <td>28716.98</td>\n",
       "      <td>28848.02</td>\n",
       "      <td>28597.00</td>\n",
       "      <td>28755.34</td>\n",
       "      <td>28621.92</td>\n",
       "      <td>12568393967</td>\n",
       "      <td>1.545223e+11</td>\n",
       "      <td>0.004661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-23</th>\n",
       "      <td>HSI</td>\n",
       "      <td>28798.45</td>\n",
       "      <td>29078.75</td>\n",
       "      <td>28748.57</td>\n",
       "      <td>29078.75</td>\n",
       "      <td>28755.34</td>\n",
       "      <td>11648087860</td>\n",
       "      <td>1.429877e+11</td>\n",
       "      <td>0.011247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      open      high       low     close  preclose  \\\n",
       "Date                                                                  \n",
       "2021-04-19    HSI  28960.13  29319.76  28806.76  29106.15  28969.71   \n",
       "2021-04-20    HSI  28962.80  29220.19  28885.55  29135.73  29106.15   \n",
       "2021-04-21    HSI  28702.27  28778.36  28506.76  28621.92  29135.73   \n",
       "2021-04-22    HSI  28716.98  28848.02  28597.00  28755.34  28621.92   \n",
       "2021-04-23    HSI  28798.45  29078.75  28748.57  29078.75  28755.34   \n",
       "\n",
       "                    vol         value       ret  \n",
       "Date                                             \n",
       "2021-04-19  12486701991  1.501631e+11  0.004710  \n",
       "2021-04-20  12337814847  1.957113e+11  0.001016  \n",
       "2021-04-21  10974966582  1.543542e+11 -0.017635  \n",
       "2021-04-22  12568393967  1.545223e+11  0.004661  \n",
       "2021-04-23  11648087860  1.429877e+11  0.011247  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfhsi.index.name='Date'\n",
    "dfhsi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "hfhsi2020 = dfhsi['2020-01-01':'2020-12-31'] # dfhsi.time.apply(lambda x:  time.strptime(x, \"%Y-%m-%d\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
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       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>HSI</td>\n",
       "      <td>26342.87</td>\n",
       "      <td>26470.40</td>\n",
       "      <td>26221.30</td>\n",
       "      <td>26409.93</td>\n",
       "      <td>26343.10</td>\n",
       "      <td>8891328219</td>\n",
       "      <td>9.438106e+10</td>\n",
       "      <td>0.002537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>HSI</td>\n",
       "      <td>26288.37</td>\n",
       "      <td>26514.08</td>\n",
       "      <td>26264.32</td>\n",
       "      <td>26314.63</td>\n",
       "      <td>26386.56</td>\n",
       "      <td>18281701925</td>\n",
       "      <td>1.907352e+11</td>\n",
       "      <td>-0.002726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>HSI</td>\n",
       "      <td>26490.45</td>\n",
       "      <td>26618.78</td>\n",
       "      <td>26432.91</td>\n",
       "      <td>26568.49</td>\n",
       "      <td>26314.63</td>\n",
       "      <td>19896101604</td>\n",
       "      <td>1.388196e+11</td>\n",
       "      <td>0.009647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>HSI</td>\n",
       "      <td>26695.11</td>\n",
       "      <td>27159.47</td>\n",
       "      <td>26670.97</td>\n",
       "      <td>27147.11</td>\n",
       "      <td>26568.49</td>\n",
       "      <td>19625523931</td>\n",
       "      <td>1.533209e+11</td>\n",
       "      <td>0.021778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>HSI</td>\n",
       "      <td>27194.79</td>\n",
       "      <td>27340.99</td>\n",
       "      <td>27163.51</td>\n",
       "      <td>27256.46</td>\n",
       "      <td>27147.11</td>\n",
       "      <td>10912546126</td>\n",
       "      <td>9.806396e+10</td>\n",
       "      <td>0.004028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol      open      high       low     close  preclose  \\\n",
       "Date                                                                  \n",
       "2020-12-24    HSI  26342.87  26470.40  26221.30  26409.93  26343.10   \n",
       "2020-12-28    HSI  26288.37  26514.08  26264.32  26314.63  26386.56   \n",
       "2020-12-29    HSI  26490.45  26618.78  26432.91  26568.49  26314.63   \n",
       "2020-12-30    HSI  26695.11  27159.47  26670.97  27147.11  26568.49   \n",
       "2020-12-31    HSI  27194.79  27340.99  27163.51  27256.46  27147.11   \n",
       "\n",
       "                    vol         value       ret  \n",
       "Date                                             \n",
       "2020-12-24   8891328219  9.438106e+10  0.002537  \n",
       "2020-12-28  18281701925  1.907352e+11 -0.002726  \n",
       "2020-12-29  19896101604  1.388196e+11  0.009647  \n",
       "2020-12-30  19625523931  1.533209e+11  0.021778  \n",
       "2020-12-31  10912546126  9.806396e+10  0.004028  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "hfhsi2020.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02    28543.52\n",
       "2020-01-03    28451.50\n",
       "2020-01-06    28226.19\n",
       "2020-01-07    28322.06\n",
       "2020-01-08    28087.92\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close = hfhsi2020['close']\n",
    "close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02         NaN\n",
       "2020-01-03    28543.52\n",
       "2020-01-06    28451.50\n",
       "2020-01-07    28226.19\n",
       "2020-01-08    28322.06\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lagclose = close.shift(1)\n",
    "lagclose.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>lagclose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>28543.52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>28451.50</td>\n",
       "      <td>28543.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>28226.19</td>\n",
       "      <td>28451.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>28322.06</td>\n",
       "      <td>28226.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>28087.92</td>\n",
       "      <td>28322.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close  lagclose\n",
       "Date                          \n",
       "2020-01-02  28543.52       NaN\n",
       "2020-01-03  28451.50  28543.52\n",
       "2020-01-06  28226.19  28451.50\n",
       "2020-01-07  28322.06  28226.19\n",
       "2020-01-08  28087.92  28322.06"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CalClose = pd.DataFrame({'close':close,'lagclose':lagclose})\n",
    "CalClose.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02         NaN\n",
       "2020-01-03   -0.003224\n",
       "2020-01-06   -0.007919\n",
       "2020-01-07    0.003396\n",
       "2020-01-08   -0.008267\n",
       "Name: Simpleret, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpleRet = close / lagclose -1\n",
    "\n",
    "simpleRet.name = 'Simpleret'\n",
    "simpleRet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>lagclose</th>\n",
       "      <th>Simpleret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>28543.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>28451.50</td>\n",
       "      <td>28543.52</td>\n",
       "      <td>-0.003224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>28226.19</td>\n",
       "      <td>28451.50</td>\n",
       "      <td>-0.007919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>28322.06</td>\n",
       "      <td>28226.19</td>\n",
       "      <td>0.003396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>28087.92</td>\n",
       "      <td>28322.06</td>\n",
       "      <td>-0.008267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close  lagclose  Simpleret\n",
       "Date                                     \n",
       "2020-01-02  28543.52       NaN        NaN\n",
       "2020-01-03  28451.50  28543.52  -0.003224\n",
       "2020-01-06  28226.19  28451.50  -0.007919\n",
       "2020-01-07  28322.06  28226.19   0.003396\n",
       "2020-01-08  28087.92  28322.06  -0.008267"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calret = pd.merge(CalClose, pd.DataFrame(simpleRet), left_index=True, right_index=True)\n",
    "calret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>lagclose</th>\n",
       "      <th>Simpleret</th>\n",
       "      <th>simpleret2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>28543.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>28451.50</td>\n",
       "      <td>28543.52</td>\n",
       "      <td>-0.003224</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>28226.19</td>\n",
       "      <td>28451.50</td>\n",
       "      <td>-0.007919</td>\n",
       "      <td>0.988883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>28322.06</td>\n",
       "      <td>28226.19</td>\n",
       "      <td>0.003396</td>\n",
       "      <td>0.995451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>28087.92</td>\n",
       "      <td>28322.06</td>\n",
       "      <td>-0.008267</td>\n",
       "      <td>0.995101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close  lagclose  Simpleret  simpleret2\n",
       "Date                                                 \n",
       "2020-01-02  28543.52       NaN        NaN         NaN\n",
       "2020-01-03  28451.50  28543.52  -0.003224         NaN\n",
       "2020-01-06  28226.19  28451.50  -0.007919    0.988883\n",
       "2020-01-07  28322.06  28226.19   0.003396    0.995451\n",
       "2020-01-08  28087.92  28322.06  -0.008267    0.995101"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpleret2 = (close)/close.shift(2)\n",
    "simpleret2.name ='simpleret2'\n",
    "\n",
    "calret['simpleret2'] = simpleret2\n",
    "calret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simpleRet.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "((1+simpleRet).cumprod()-1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014652498813569587"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpleRet.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02304279179850761"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpleRet.quantile(0.05) # 含义： 有5%的可能性下跌超过 1.96431486803879%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0241799426650797"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "norm.ppf(0.05, simpleRet.mean(), simpleRet.std())  # 协方差矩阵法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
